2 code implementations • NeurIPS 2018 • Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires.
no code implementations • 7 Dec 2018 • Tobias Johannink, Shikhar Bahl, Ashvin Nair, Jianlan Luo, Avinash Kumar, Matthias Loskyll, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine
In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL.
2 code implementations • ICML 2020 • Vitchyr H. Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills.
1 code implementation • 13 Jun 2019 • Gerrit Schoettler, Ashvin Nair, Jianlan Luo, Shikhar Bahl, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine
Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction.
1 code implementation • 23 Oct 2019 • Ashvin Nair, Shikhar Bahl, Alexander Khazatsky, Vitchyr Pong, Glen Berseth, Sergey Levine
When the robot's environment and available objects vary, as they do in most open-world settings, the robot must propose to itself only those goals that it can accomplish in its present setting with the objects that are at hand.
no code implementations • NeurIPS 2020 • Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta, Deepak Pathak
We show that NDPs outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks for both imitation and reinforcement learning setups.
Ranked #4 on Meta-Learning on MT50
no code implementations • 12 Jul 2021 • Shikhar Bahl, Abhinav Gupta, Deepak Pathak
We tackle the problem of generalization to unseen configurations for dynamic tasks in the real world while learning from high-dimensional image input.
no code implementations • 19 Jul 2022 • Shikhar Bahl, Abhinav Gupta, Deepak Pathak
We approach the problem of learning by watching humans in the wild.
no code implementations • 8 Dec 2022 • Kenneth Shaw, Shikhar Bahl, Deepak Pathak
We build a learning algorithm, VideoDex, that leverages visual, action, and physical priors from human video datasets to guide robot behavior.
no code implementations • 13 Feb 2023 • Russell Mendonca, Shikhar Bahl, Deepak Pathak
Robotic agents that operate autonomously in the real world need to continuously explore their environment and learn from the data collected, with minimal human supervision.
no code implementations • CVPR 2023 • Shikhar Bahl, Russell Mendonca, Lili Chen, Unnat Jain, Deepak Pathak
Utilizing internet videos of human behavior, we train a visual affordance model that estimates where and how in the scene a human is likely to interact.
no code implementations • 21 Aug 2023 • Russell Mendonca, Shikhar Bahl, Deepak Pathak
We propose an approach for robots to efficiently learn manipulation skills using only a handful of real-world interaction trajectories from many different settings.
no code implementations • 5 Sep 2023 • Kevin Gmelin, Shikhar Bahl, Russell Mendonca, Deepak Pathak
Agents that are aware of the separation between themselves and their environments can leverage this understanding to form effective representations of visual input.
1 code implementation • 30 Oct 2023 • Aditya Kannan, Kenneth Shaw, Shikhar Bahl, Pragna Mannam, Deepak Pathak
In this paper, we investigate these challenges, especially in the case of soft, deformable objects as well as complex, relatively long-horizon tasks.
no code implementations • 7 Dec 2023 • Lili Chen, Shikhar Bahl, Deepak Pathak
To make diffusion models more useful for skill learning, we encourage robotic agents to acquire a vocabulary of skills by introducing discrete bottlenecks into the conditional behavior generation process.